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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley

We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. (2003), The Impact of Trade on Intra-Industry Reallocations and. Robust Regression and Outlier Detection by Peter J. Tries to devise estimators that are not so strongly affected by outliers. Robust Nonlinear Fitting by RANSAC (Outlier Tolerent Nonlinear Regression). I am have been working on a more robust regression boosting algorithm for my undergraduate thesis. Robust Nonlinear Optimization MATLAB Code Download Please have a look in RANSAC_4_Nonlinear.m This code is not heavily tested. Leroy (1987), Robust Regression and Outlier. I always think, "This is a univariate analysis! Aggregate Industry Productivity. Furthermore, a support vector regression (SV regression) approach is proposed for simultaneous data reconciliation and gross error or outlier detection, which considers gross errors and outliers as model complexity so as to remove them. I had a discussion the other day about using the weights returned by boosting to do outlier detection. "Robust Regression and Outlier Detection" states "robustregression . Leroy · Tweetear Book Details: Book Title: Robust Regression and Outlier Detection Author: Peter J. One way is to call the ROBUSTREG procedure! The ROBUSTREG procedure provides four different How can you detect univariate outliers in SAS? Why am I using However, you can also use the ROBUSTREG procedure to estimate robust statistics. Econometrica 71 (6), 1695-1725. I've conducted a lot of univariate analyses in SAS, yet I'm always surprised when the best way to carry out the analysis uses a SAS regression procedure. For data reconciliation, the SV regression Moreover, it is not so strict to tune the coefficients of the SV regression approach because of the robustness of the coefficients for the reconciled results.